The Economic Value of Predicting Stock Index Returns and Volatility

In this paper, we analyze the economic value of predicting index returns as well as volatility. On the basis of fairly simple linear models, estimated recursively, we produce genuine out-of-sample forecasts for the return on the S&P 500 index and its volatility. Using monthly data from 1954-1998, we test the statistical significance of return and volatility predictably and examine the economic value of a number of alternative trading strategies. We find strong evidence for market timing in both returns and volatility. Joint tests indicate no dependence between return and volatility timing, while it appears easier to forecast returns when volatility is high. For a mean-variance investor, this predictably is economically profitable, even if short sales are not allowed and transaction costs are quite large.Predicability of stock returns and volatility

Log Skeletons: A Classification Approach to Process Discovery

To test the effectiveness of process discovery algorithms, a Process Discovery Contest (PDC) has been set up. This PDC uses a classification approach to measure this effectiveness: The better the discovered model can classify whether or not a new trace conforms to the event log, the better the discovery algorithm is supposed to be. Unfortunately, even the state-of-the-art fully-automated discovery algorithms score poorly on this classification. Even the best of these algorithms, the Inductive Miner, scored only 147 correct classified traces out of 200 traces on the PDC of 2017. This paper introduces the rule-based log skeleton model, which is closely related to the Declare constraint model, together with a way to classify traces using this model. This classification using log skeletons is shown to score better on the PDC of 2017 than state-of-the-art discovery algorithms: 194 out of 200. As a result, one can argue that the fully-automated algorithm to construct (or: discover) a log skeleton from an event log outperforms existing state-of-the-art fully-automated discovery algorithms.Comment: 16 pages with 9 figures, followed by an appendix of 14 pages with 17 figure

A framework for algorithm stability

We say that an algorithm is stable if small changes in the input result in small changes in the output. Algorithm stability plays an important role when analyzing and visualizing time-varying data. However, so far, there are only few theoretical results on the stability of algorithms, possibly due to a lack of theoretical analysis tools. In this paper we present a framework for analyzing the stability of algorithms. We focus in particular on the tradeoff between the stability of an algorithm and the quality of the solution it computes. Our framework allows for three types of stability analysis with increasing degrees of complexity: event stability, topological stability, and Lipschitz stability. We demonstrate the use of our stability framework by applying it to kinetic Euclidean minimum spanning trees

Predicting Dropout From Organized Football:A Prospective 4-Year Study Among Adolescent and Young Adult Football Players

Previous studies have shown that enjoyment is one of the key predictors of dropout from organized sport, including organized football. However, prospective studies, particularly studies focused on long-term dropout, are largely lacking. Drawing on the basic principles of interdependence theory, in the present prospective study among 1,762 adolescent and young adult football players (27.1% women, mean age 17.74 years, SD = 1.35), we tested the predictive value of sport enjoyment, perceived alternatives, and restraining forces on football players' short-term (6 months) and long-term (4 years) dropout from organized football. As anticipated, the results of the logistic regression and follow-up analyses indicate that players' enjoyment was the main predictor of (short-term and long-term) dropout. In addition, relative to remainers, dropouts perceived more alternatives in terms of other sports, had fewer family members involved in their football club, and were older at the time they started playing organized football. We conclude that particularly measures aimed at enhancing sport enjoyment may prevent players from dropping out from organized football in both the short and long term. In addition, dropout rates may be reduced by attracting and engaging youth at a very young age (from 6 years), and their siblings, parents, and other family members as well